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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

Showing 361370 of 1277 papers

TitleStatusHype
Extended Deep Submodular Functions0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
Diversity-Driven View Subset Selection for Indoor Novel View SynthesisCode0
Machine Learning and Constraint Programming for Efficient Healthcare Scheduling0
Deep Generative Model for Mechanical System Configuration Design0
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning0
Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based SamplingCode0
A GREAT Architecture for Edge-Based Graph Problems Like TSPCode0
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization0
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing0
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